Adverse effects and SARS-CoV-2 infection after COVID-19 vaccination among the vaccinated people of Bangabandhu Sheikh Mujib Medical University Hospital in Dhaka, Bangladesh: a pilot study
Bibliographic record
Abstract
The Pfizer, Moderna, AstraZeneca, and Sinopharm COVID-19 vaccines have demonstrated robust safety and efficacy in Phase II clinical trials. We aimed to assess adverse effects and post-vaccination infection rates amongst vaccinated people at Bangabandhu Sheikh Mujib Medical University (BSMMU). In this prospective observational follow-up pilot study, 2534 subjects aged below 20 years or older who had received two doses of the Pfizer, Moderna, AstraZeneca, and Sinopharm COVID-19 vaccines were selected using a consecutive sampling method from June to November 2021. We evaluated the local and systemic side effects of the subjects. The subjects under study were followed up within seven days and up to 28 days after 1st and 2nd dose of all four vaccine. We also assessed post-vaccination infection rates among individuals tested for SARS-CoV-2 via PCR or rapid antigen tests. The demographic variables, type of administered vaccine, adverse effects, and presence of comorbidities were collected. Descriptive statistics (mean, standard deviation) were performed by using SPSS. Among 2534 participants who were vaccinated, systemic side effects were reported by 14.4% (379/2534) after the first dose and 10.4% (265/2534) after the second dose across all vaccines. Among vaccine-specific groups, systemic side effects were observed in 6.6% (109/1635) and 5.2% (85/1635) for the first and second doses of Pfizer; 34.3% (136/396) and 42% (166/396) for Moderna; 6.5% (32/488) and 2.9% (14/488) for Sinopharm; and 13.3% (2/15) for AstraZeneca after both doses. Local side effects were reported in 4.8% (79/1635) and 2% (32/1635) for the first and second doses of Pfizer; 45.5% (82/396) and 26% (106/396) for Moderna; 10.7% (53/488) and 2.6% (13/488) for Sinopharm; and primarily local effects for AstraZeneca. Post-vaccination infection among the vaccinated participants across all vaccines were ranging from 0.1 to 0.3% after the first and second doses.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".